Flash flooding is one of the periodic geohazards in the eastern desert of Egypt where many parts of Upper Egypt, Sinai, and Red Sea areas were hit by severe flash floods, for example in 1976, 1982, 1996 and January 2010. The hazard degree for each sub-basin was determined using the approach developed by El-Shamy for assessing susceptibility of sub-basins to flash flooding risk. To identify at-risk sub-basins, two different methods were applied. The first method is based on the relationship between the drainage density and bifurcation ratio, and the second one uses the relationship between drainage frequency and bifurcation ratio. The three morphometric parameters (the bifurcation ratio, drainage density, and stream frequency) were extracted and calculated for each sub-basin of the watershed. Based on the final hazard degree resulting from the two methods, a detailed hazard degree map was extracted for all sub-basins. The results illustrate that there are no sub-basins with low risk of flooding. The sub-basins with the highest hazard degree are concentrated in the middle of the watershed although they have smaller areas compared with the surrounding sub-basins. The sub-basins located at the boundary of the watershed have an intermediate risk of flooding and moderate potential for groundwater recharge. This constructed map can be used as a basic data for assessment of flood mitigation and planning.

Flooding is one of the most frequent and destructive natural disasters in Japan, particularly in river basins with high population density and urbanization. This study aimed to use a GIS-based probabilistic Certainty Factor (CF) model to evaluate flood susceptibility in the Arakawa River basin, Japan. Nineteen flood conditioning factors were incorporated: soil, land use/land cover (LULC), normalized difference built-up index (NDBI), normalized difference water index (NDWI), normalized difference vegetation index (NDVI), curvature, elevation, precipitation, slope, topographic position index (TPI), sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), drainage density (Dd), distance to streams, distance to roads, flow accumulation, population, and aspect were included to assess their impact on flood frequency. A flood susceptibility map (FSM) was generated by applying the Certainty Factor model. A total of 230 flood locations within the study area were examined and geostatistically processed in ArcGIS for model validation. The resulting FSM was categorized into five susceptibility classes: very low, low, moderate, high, and very high. The spatial distribution of these classes showed that 22.5% of the area falls under moderate susceptibility, 37.1% under high, and 22.6% under very high susceptibility. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), yielding an accuracy of approximately 71%. The results indicate that the most influential factors affecting flood susceptibility in the basin are elevation, stream power index (SPI), sediment transport index (STI), flow accumulation, and distance to roads. The suggested framework offers useful spatial insights that can assist in supporting decision-makers to reduce both economic losses and risks to human life.

Flood susceptibility mapping is essential for effective flood mitigation and land management, particularly in rapidly urbanizing watersheds. This study proposes a multi-criteria framework for improving flood susceptibility modeling through conditioning factor selection. Nineteen topographic, hydrological, environmental, and land-use factors were used to develop four models: Frequency Ratio (FR), Shannon Entropy (SE), Certainty Factor (CF), and VIKOR. A flood inventory of 230 historical flood locations was compiled, with 160 locations (70%) used for model development and 70 (30%) reserved for independent validation. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis based on Success Rate and Prediction Rate datasets. A factor selection framework integrating ROC-AUC, Information Value (IV), Pearson correlation, and Variance Inflation Factor (VIF) was then applied to identify informative and non-redundant variables. The framework reduced the conditioning factors from 19 to 9. All optimized models showed improved predictive performance. The FR model showed the greatest improvement, with Success Rate AUC increasing from 58.45% to 90.41% and Prediction Rate AUC from 58.10% to 89.88%. The CF model achieved the highest overall performance after optimization, while SE and VIKOR also improved substantially. The proposed framework improves prediction while reducing input-data complexity.